Boris Epshtein

Detecting text in natural scenes with stroke width transform

We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.

Hierarchical photo organization using geo-relevance

We present a novel framework for organizing large collections of images in a hierarchical way, based on scene semantics. Rather than score images directly, we use them to score the scene in order to identify typical views and important locations which we term Geo-Relevance. This is done by relating each image with its viewing frustum which can be readily computed for huge collections of images nowadays. The frustum contains much more information than only camera position that has been used so far. For example, it distinguishes between a photo of the Eiffel Tower and a photo of a garbage bin taken from the exact same place. The proposed framework enables a summarized display of the information and facilitates efficient browsing.